Businesses collect data representing various aspects of their activities, for example, transactions performed, user interactions with a website of the business, inventory, employee information, and so on. This data is generated by various sources, for example, by systems and applications used by the business, provided by third parties, manually entered, and so on. As an example, a particular system may be used for managing sales of the business, another system may be used for managing inventory, another system for employee information, and so on. Each system or application acts as a data source of information that describes a particular aspect or aspects of the business.
Business experts analyze data from these data sources to make informed business decisions. An executive may analyze the information to make strategic decisions for the business, for example, the amount of resources to be allocated for promoting certain products, type of advertising campaigns to be used for product promotion, and other business decisions. Data coming from a variety of data sources is often complex to analyze. Typically there are complex logical relationships that connect different types of data. Analysis of such data requires understanding of these relationships. Business experts that typically understand these relationships often do not have background in statistics, business intelligence, and/or data visualization.
Conventional tools for performing data analysis and visualization require strong background in statistics, query languages, data visualization techniques, and so on. These sophisticated users of the data analysis tools are referred to as data analysts (or data scientists or data engineers.) Business experts typically provide requirements to the data analysts to perform the data analysis. The data analysts perform the data analysis for the business experts and provide the information.
There is often loss of information as a result of communication gaps between the data analysts and the business experts leading to incorrect reports being generated as a result of data analysts misinterpreting the requirements provided by the business experts or the business experts misinterpreting the reports provided by the data analysts. These errors lead to incorrect data analysis being performed by the business experts that may lead to the business expert making incorrect strategic decisions. Furthermore, conventional systems require the business expert to rely on the data analysts to perform the data analysis for them. Data analysts perform the data analysis without having the context or the expertise that the business experts have. As a result there are long delays in the time that the business expert requests certain information and the time that the data analysts provide the requested information. As a result, the data analyst often becomes a bottleneck in the data analysis process. Often business experts that need to make time sensitive decisions forego the information provided by the data analysts since it arrives too late for use. The business expert may use the information provided by the data analyst for validation or confirmation of their decisions ex post facto. However, the information provided by the data analysts loses its main purpose of guiding the decision making process of the business expert. As a result, conventional data analysis and visualization tools are often inadequate for bridging the gap between data analysts and the business experts or for providing timely information to business experts and do not provide the functionality needed by the users performing the analysis of the business data.